Experimental Evaluation of Adaptive Beamforming for Automotive Radar Interference Suppression

Rameez, Muhammad; Dahl, Mattias; Pettersson, Mats I.

doi:10.1109/rws45077.2020.9049982

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…In [13], the authors proposed an interference mitigation method using an adaptive canceller to minimize the correlation between the waveforms of the victim and interfering radars. Besides the methos performed in timedomain, a spatial-domain detector design for mutual interference mitigation among automotive MIMO-FMCW radars was proposed in [14], and a Least Mean Squares (LMS) algorithm-based adaptive beamformer was investigated in [15].…”

Section: Introductionmentioning

confidence: 99%

Experimental Evaluations on Learning-Based Inter-Radar Wideband Interference Mitigation Method

KOIZUMI,

WANG,

UMEHIRA

et al. 2024

IEICE Trans. Fundamentals

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In recent years, high-resolution 77GHz band automotive radar, which is indispensable for autonomous driving, has been extensively investigated. In the future, as vehicle-mounted CS (chirp sequence) radars become more and more popular, intensive inter-radar wideband interference will become a serious problem, which results in undesired miss detection of targets. To address this problem, learning-based wideband interference mitigation method has been proposed, and its feasibility has been validated by simulations. In this paper, firstly we evaluated the tradeoff between interference mitigation performance and model training time of the learning-based interference mitigation method in a simulation environment. Secondly, we conducted extensive inter-radar interference experiments by using multiple 77GHz MIMO (Multiple-Input and Multiple-output) CS radars and collected real-world interference data. Finally, we compared the performance of learning-based interference mitigation method with existing algorithm-based methods by real experimental data in terms of SINR (signal to interference plus noise ratio) and MAPE (mean absolute percentage error).

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Section: Introductionmentioning

confidence: 99%

Experimental Evaluations on Learning-Based Inter-Radar Wideband Interference Mitigation Method

KOIZUMI,

WANG,

UMEHIRA

et al. 2024

IEICE Trans. Fundamentals

View full text Add to dashboard Cite

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“…At the RX end, it is possible to mitigate the effect of interference by applying signal processing methods on the received signal. If antenna arrays are available, then interference can be suppressed in the spatial domain by using digital beamforming [15]- [17]. Interference can also be suppressed in the time domain by detecting and zeroing out the disturbed samples in the received signal [18].…”

Section: Introductionmentioning

confidence: 99%

Autoregressive Model-Based Signal Reconstruction for Automotive Radar Interference Mitigation

2021

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Spatial-Domain Interference Mitigation for Slow-Time MIMO-FMCW Automotive Radar

Jin

Wang

Boufounos

et al. 2022

2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM)

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This paper considers mutual interference mitigation among automotive radars using frequency-modulated continuous wave (FMCW) signal and multiple-input multiple-output (MIMO) virtual arrays. For the first time, we derive a general interference signal model that fully accounts for not only the time-frequency incoherence, e.g., different FMCW configuration parameters and time offsets, but also the slow-time code MIMO incoherence and array configuration differences between the victim and interfering radars. Along with a standard MIMO-FMCW object signal model, we turn the interference mitigation into a spatial-domain object detection under incoherent MIMO-FMCW interference described by the explicit interference signal model, and propose a constant false alarm rate (CFAR) detector. More specifically, the proposed detector exploits the structural property of the derived interference model at both transmit and receive steering vector space. We also derive analytical closedform expressions for probabilities of detection and false alarm. Performance evaluation using both synthetic-level and phased array system-level simulation confirms the effectiveness of our proposed detector over selected baseline methods.

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Experimental Evaluation of Adaptive Beamforming for Automotive Radar Interference Suppression

Cited by 4 publications

References 4 publications

Experimental Evaluations on Learning-Based Inter-Radar Wideband Interference Mitigation Method

Experimental Evaluations on Learning-Based Inter-Radar Wideband Interference Mitigation Method

Autoregressive Model-Based Signal Reconstruction for Automotive Radar Interference Mitigation

Spatial-Domain Interference Mitigation for Slow-Time MIMO-FMCW Automotive Radar

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